Convert dataset to Parquet

README.md

@@ -9,7 +9,6 @@ license:
 - unknown
 multilinguality:
 - monolingual
-pretty_name: BookCorpus
 size_categories:
 - 10M<n<100M
 source_datasets:
@@ -21,17 +20,23 @@ task_ids:
 - language-modeling
 - masked-language-modeling
 paperswithcode_id: bookcorpus
+pretty_name: BookCorpus
 dataset_info:
+  config_name: plain_text
   features:
   - name: text
     dtype: string
-  config_name: plain_text
   splits:
   - name: train
-    num_bytes: 4853859824
+    num_bytes: 4835329162
     num_examples: 74004228
-  download_size: 1179510242
-  dataset_size: 4853859824
+  download_size: 3019488297
+  dataset_size: 4835329162
+configs:
+- config_name: plain_text
+  data_files:
+  - split: train
+    path: plain_text/train-*
 ---
 
 # Dataset Card for BookCorpus

dataset_infos.json

@@ -1 +1,34 @@
-{"plain_text": {"description": "Books are a rich source of both fine-grained information, how a character, an object or a scene looks like, as well as high-level semantics, what someone is thinking, feeling and how these states evolve through a story.This work aims to align books to their movie releases in order to providerich descriptive explanations for visual content that go semantically farbeyond the captions available in current datasets. ", "citation": "@InProceedings{Zhu_2015_ICCV,\n    title = {Aligning Books and Movies: Towards Story-Like Visual Explanations by Watching Movies and Reading Books},\n    author = {Zhu, Yukun and Kiros, Ryan and Zemel, Rich and Salakhutdinov, Ruslan and Urtasun, Raquel and Torralba, Antonio and Fidler, Sanja},\n    booktitle = {The IEEE International Conference on Computer Vision (ICCV)},\n    month = {December},\n    year = {2015}\n}\n", "homepage": "https://yknzhu.wixsite.com/mbweb", "license": "", "features": {"text": {"dtype": "string", "id": null, "_type": "Value"}}, "supervised_keys": null, "builder_name": "bookcorpus", "config_name": "plain_text", "version": {"version_str": "1.0.0", "description": "", "datasets_version_to_prepare": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 4853859824, "num_examples": 74004228, "dataset_name": "bookcorpus"}}, "download_checksums": {"https://storage.googleapis.com/huggingface-nlp/datasets/bookcorpus/bookcorpus.tar.bz2": {"num_bytes": 1179510242, "checksum": "03a29333b2b35e98a375b6ad7d2f1651835655cd348fb89c864136bce69a964c"}}, "download_size": 1179510242, "dataset_size": 4853859824, "size_in_bytes": 6033370066}}
+{
+    "plain_text": {
+        "description": "Books are a rich source of both fine-grained information, how a character, an object or a scene looks like, as well as high-level semantics, what someone is thinking, feeling and how these states evolve through a story.This work aims to align books to their movie releases in order to providerich descriptive explanations for visual content that go semantically farbeyond the captions available in current datasets. ",
+        "citation": "@InProceedings{Zhu_2015_ICCV,\n    title = {Aligning Books and Movies: Towards Story-Like Visual Explanations by Watching Movies and Reading Books},\n    author = {Zhu, Yukun and Kiros, Ryan and Zemel, Rich and Salakhutdinov, Ruslan and Urtasun, Raquel and Torralba, Antonio and Fidler, Sanja},\n    booktitle = {The IEEE International Conference on Computer Vision (ICCV)},\n    month = {December},\n    year = {2015}\n}\n",
+        "homepage": "https://yknzhu.wixsite.com/mbweb",
+        "license": "",
+        "features": {
+            "text": {
+                "dtype": "string",
+                "_type": "Value"
+            }
+        },
+        "builder_name": "parquet",
+        "dataset_name": "bookcorpus",
+        "config_name": "plain_text",
+        "version": {
+            "version_str": "1.0.0",
+            "major": 1,
+            "minor": 0,
+            "patch": 0
+        },
+        "splits": {
+            "train": {
+                "name": "train",
+                "num_bytes": 4835329162,
+                "num_examples": 74004228,
+                "dataset_name": null
+            }
+        },
+        "download_size": 3019488297,
+        "dataset_size": 4835329162,
+        "size_in_bytes": 7854817459
+    }
+}

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